Predicting plant diversity in beach wetland downstream of Xiaolangdi reservoir with UAV and satellite multispectral images

Zhu, Hui; Huang, Yanwei; Li, Yingchen; Yu, Fei; Zhang, Jianan; Fan, Linlin; Zhou, Jiahui; Yuan, Meng

doi:10.1016/j.scitotenv.2022.153059

Cited by 16 publications

(4 citation statements)

References 71 publications

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“…NDVI is known for its sensitivity to primary productivity, which defines spatial variations in plant diversity (Stoms & Estes, 1993;Gillespie, 2005). Numerous studies have demonstrated significant correlations between NDVI and species diversity in various regions, such as savannah biomes (Madonsela et al, 2018) and wetlands (Zhu et al, 2022). However, our study reveals that NDVI may not always be a reliable proxy for measuring diversity, DVI can be used to establish a mathematical model for monitoring vegetation diversity at our study site.…”

Section: Feasibility Of Uav-based Vegetation Diversity Monitoringmentioning

confidence: 77%

“…Studies estimating plant diversity using remote sensing can typically be divided into two categories: direct identification of plant species and their distribution through visual interpretation or image classification algorithms, and indirect methods that establish a relationship between diversity and spectral data, or derive species distribution through habitat mapping (Rocchini, 2007;Madonsela et al, 2017;Wang and Gamon, 2019;Villoslada et al, 2020;Zhu et al, 2022). Many studies use vegetation indices such as NDVI estimate species diversity indirectly, although they do not discriminate well between vegetation communities (Gillespie, 2005;Madonsela et al, 2018;Kacic and Kuenzer, 2022;Tian and Fu, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Using Unmanned Aerial Vehicle multispectral data for monitoring outcomes of ecological restoration in mining areas

Chen

Hou

Zhang

et al. 2023

Preprint

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The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labour intensive manual methods which can be difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify α-diversity index on a sub-meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semi-automate large-scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry-standard supervised classification algorithms (support vector machine (SVM), maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (NDVI, DVI, VDVI, SAVI, MSAVI, EXG - EXR) were used to assess vegetation diversity obtained from on-ground survey plot data (Margalef, Pielou, Simpson, Shannon indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy 84%). Significant relationships were observed between vegetation indices and diversity indices, with DVI performing significantly better than many more commonly used indices such as NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine-scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools.

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Section: Feasibility Of Uav-based Vegetation Diversity Monitoringmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Using Unmanned Aerial Vehicle multispectral data for monitoring outcomes of ecological restoration in mining areas

Chen

Hou

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The NDVI is known for its sensitivity to primary productivity, which defines spatial variations in plant diversity (Gillespie, 2005; Stoms & Estes, 1993). Numerous studies have demonstrated significant correlations between the NDVI and species diversity in various regions, such as savannah biomes (Madonsela et al, 2018) and wetlands (Zhu et al, 2022). However, our study reveals that the NDVI may not always be a reliable proxy for measuring diversity, the DVI can be used to establish a mathematical model for monitoring vegetation diversity at our study site.…”

Section: Discussionmentioning

confidence: 99%

“…Studies estimating plant diversity using remote sensing can typically be divided into two categories: direct and indirect approaches. Direct approaches underscore habitat and species mapping with the identification of species; on the other hand, indirect approaches use indices such as functional or spectral diversity as the basis for building models of species distributions and the spatial arrangement of diversity (Madonsela et al, 2017; Rocchini, 2007; Villoslada et al, 2020; Wang & Gamon, 2019; Zhu et al, 2022). For the assessment of vegetation diversity, multiple studies used normalized difference vegetation index (NDVI) as input metrics to estimate spectral α diversity based on spaceborne remote sensing, primarily due to its correlation with Net‐primary productivity (Gillespie, 2005; Kacic & Kuenzer, 2022; Madonsela et al, 2018; Tian & Fu, 2022; Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

Chen,

Hou,

Zhang

et al. 2023

Land Degrad Dev

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The effective and efficient monitoring of revegetation outcomes is a key component of ecosystem restoration. Monitoring often involves labor‐intensive manual methods, which are difficult to deploy when sites are inaccessible or involve large areas of revegetation. This study aimed to identify plant species and quantify α‐diversity index on a sub‐meter scale at Manlailiang Mine Site in Northwestern China using unmanned aerial vehicles (UAVs) as a means to semiautomate large‐scale vegetation monitoring. UAVs equipped with multispectral sensors were combined with three industry‐standard supervised classification algorithms (support vector machine [SVM], maximum likelihood, and artificial neural network) to classify plant species. Spectral vegetation indices (normalized difference vegetation index [NDVI], difference vegetation index [DVI], visible‐band difference vegetation index, soil‐adjusted vegetation index, modified soil‐adjusted vegetation index, and excess green–excess red) were used to assess vegetation diversity obtained from on‐ground survey plot data (the Margalef, Pielou, Simpson, and Shannon–Wiener indices). Our results showed that SVM outperformed other algorithms in species identification accuracy (overall accuracy of 84%). Significant relationships were observed between vegetation indices and diversity indices, with the DVI performing significantly better than many more commonly used indices such as the NDVI. The findings highlight the potential of combining UAV multispectral data, spectral vegetation indices and ground surveys for effective and efficient fine‐scale monitoring of vegetation diversity in the ecological restoration of mining areas. This has significant practical benefits for improving adaptive management of restoration through improved monitoring tools.

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Using UAV multispectral photography to discriminate plant species in a seep wetland of the Fynbos Biome

Musungu,

Dube,

Smit

et al. 2024

Wetlands Ecol Manage

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Wetlands harbour a wide range of vital ecosystems. Hence, mapping wetlands is essential to conserving the ecosystems that depend on them. However, the physical nature of wetlands makes fieldwork difficult and potentially erroneous. This study used multispectral UAV aerial photography to map ten wetland plant species in the Fynbos Biome in the Steenbras Nature Reserve. We developed a methodology that used K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Random Forest (RF) machine learning algorithms to classify ten wetland plant species using the preselected bands and spectral indices. The study identified Normalized green red difference index (NGRDI), Red Green (RG) index, Green, Log Red Edge (LogRE), Normalized Difference Red-Edge (NDRE), Chlorophyll Index Red-Edge (CIRE), Green Ratio Vegetation Index (GRVI), Normalized Difference Water Index (NDWI), Green Normalized Difference Vegetation Index (GNDVI) and Red as pertinent bands and indices for classifying wetland plant species in the Proteaceae, Iridaceae, Restionaceae, Ericaceae, Asteraceae and Cyperaceae families. The classification had an overall accuracy of 87.4% and kappa accuracy of 0.85. Thus, the findings are pertinent to understanding the spectral characteristics of these endemic species. The study demonstrates the potential for UAV-based remote sensing of these endemic species.

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Predicting plant diversity in beach wetland downstream of Xiaolangdi reservoir with UAV and satellite multispectral images

Cited by 16 publications

References 71 publications

Using Unmanned Aerial Vehicle multispectral data for monitoring outcomes of ecological restoration in mining areas

Using Unmanned Aerial Vehicle multispectral data for monitoring outcomes of ecological restoration in mining areas

Using unmanned aerial vehicle multispectral data for monitoring the outcomes of ecological restoration in mining areas

Using UAV multispectral photography to discriminate plant species in a seep wetland of the Fynbos Biome

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